1. Field of the Invention
The present invention relates to a method of calibrating ultrasound velocity and more particularly, relates to a method of calibrating ultrasound velocity by finding locations of two sub-apertures and performing a Mean Absolute Percentage Error (MAPE) process on two sub-aperture images to obtain calibrated ultrasound velocity.
2. Description
The technique of generating images by means of ultrasound has been widely adopted in biomedical applications. Compared with other medical imaging systems such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI) and nuclear medicine imaging utilized in clinic, ultrasonic imaging has advantages of cost effectiveness, non-invasiveness, no ionizing radiation, real-time imaging capability, high spatial resolution (less than 1 sub-millimeter), portability, flow estimation ability, etc. Thus, ultrasound imaging has been commonly applied to clinical diagnosis in several medical categories.
The principle of ultrasound imaging is to reconstruct an image of an object to be detected with the characteristics of diffraction and reflection of sound waves. Specifically, the principle of ultrasound imaging is to emit sound waves into a human body via a probe; the sound waves interact with all kinds of media in the human body; and users reconstruct images inside the human body according to signals echoed. Recently, ultrasound system of arrays have been widely used in diagnosis and applications, as better image quality can be achieved by adjusting a probe unit for time differences. However, human tissues are not evenly distributed, which affects imaging results. Velocity is an important physical parameter. With mean sounding velocity imaging, problems of bad quality of images due to uneven human tissues are mitigated. Quality of images includes spatial resolution, contrast resolution and even diagnosis of pathological location. In current ultrasound array imaging systems, a default mean velocity (e.g. 1540 m/s) is used for imaging. The difference between the default mean velocity and the actual mean velocity will affect the quality of images and further affect the diagnosis.
Therefore, methods of calibrating velocities have been brought up to obtain a mean velocity which is close to the actual mean velocity in order to improve the quality of images. Current methods of calibration include analyzing the 2D auto-covariance function of images and determining the alignment of delayed data among every unit. In terms of analyzing the 2D auto-covariance function of images, imaging of every velocity is performed in different time; images of every velocity are gathered and a 2D auto-covariance function graph is made according to random image information of focus depth, from which distribution functions are obtained, Full Width Half Maximum (FWHM) of energy (−6 dB) is found and compared with every velocity within the velocity range; a graph showing the trend of the FWHM of energy and every velocity is obtained. Theoretically, the better the resolution is, the smaller the width should be; therefore, it can be observed that the width of the FWHM of energy (−6 dB) will be smaller when being close to the actual velocity. However, data volume of images of every velocity is very heavy and every velocity within the velocity range needs to be compared; as a result, the operation time is too long and not effective.
In terms of determining the alignment of delayed data among every unit, after receiving the echoed ultrasound signals, signals which have been demodulated to fundamental frequency are time-delayed through different velocities; phases of different data are calculated according to every adjacent probe unit; standard deviation is obtained to calculate the alignment; finally, a trend graph showing the relation between the standard deviation and its corresponding velocity range is drawn. Theoretically, in the place close to the mean actual velocity there should be smaller standard deviation and better alignment; on the contrary, places farther from the mean actual velocity have greater standard deviation and worse alignment. However, whether the data are aligned is determined by means of velocity iteration; but practically when there is a small deviation among the alignment of data, the accuracy of following data will be affected, which takes longer data operation time and is inaccurate, and further affects its application in clinical diagnosis.